Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2015-2016

C O N T E N TS
■ Message from Director-General ——

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■ Institute Overview—

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■ ISM Projects ——NOE (Network Of Excellence) Project
Project for Fostering and Promoting
Statistical Thinking
Coop with Math Program
Data Scientist Training Network

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■ Research Cooperation——
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■ Graduate School Program—
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■ Facilities and Equipment——
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■ Finance and Buildings——
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■ Organization——
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■ History—

Message from Director-General

The Institute of Statistical Mathematics (hereinafter, ISM) was founded in 1944, not long before the end of World War II. Its original statedpurpose was “to provide supervision for studies looking into the mathematical principles of probability and their application, and to facilitate,
unify, and promote the publication of research results”. Ever since its
establishment, at a time of very conscious awareness of the realities of
the world, ISM has steadily absorbed this attitude into its DNA, as it hascontributed to the deeper understanding and advancement of statisticalmathematics. We enter the final year of our second medium-term planning period as an Inter-University Research Institute Corporation, at atime when national research and educational institutions are facing avariety of pressing challenges, and so we are thinking ahead to our third
medium-term goals and plans next year, through active consultationand exploration of how we can provide the greatest value to the various
communities that we serve as an Inter-University Research Institute.
Following on from my first term (four years), I have been appointed as
Director-General of ISM for a further term of two years, beginning this year. In my first four years, I focusedmuch of my modest energies in getting ISM’s two biggest projects—“NOE (Network Of Excellence) Project”
and “Project for Fostering and Promoting Statistical Thinking”—off the ground and up and running stably. I
would say that in terms of fulfilling ISM’s duty as one of the Inter-University Research Institutes, which is “toadvance joint usage and joint research functions”, we’re proud of the results that we have achieved.
Two of the main points of our operation policy in this final year of the second medium-term planning period
are “consolidating organizational strength” and “promoting concrete collaborative initiatives with other institutes”. To address the first of these goals, at the start of my second term as Director-General, we have reshuffled the personnel of executive directors of ISM by adding some younger people than those whom we have
had in the past, to tackle the running of ISM with fresh, new energy. Under this new system, we are working
on a variety of improvement measures, such as reorganizing an Information and Public Relations Unit, providing URA (University Research Administrator) functions to various sections of ISM, and increasing theorganizational capabilities of our administration.
On the point of “promoting concrete collaborative initiatives with other institutes”, we make available our
supercomputer systems newly introduced over the previous year to the various communities—including bymeans of participating in the innovative “High-Performance Computing Infrastructure (HPCI)” project toprovide one of our supercomputer system as the first Inter-University Research Institute, and we deepen our
relationships with national university-affiliated research institutes and centers across Japan, National Research and Development Agencies, academic societies and education-related organizations through the“Coop with Math Program”, a program undertaken on contract for the Ministry of Education, Culture, Sports,
Science and Technology (MEXT), and also through the “Data Scientist Training Network”. These activities
lead to the formulation of concrete collaborative initiatives. In addition, we make a special effort to connect
with the research divisions of private companies, using “Data Science Research Plaza”, the new program of“Project for Fostering and Promoting Statistical Thinking”. Furthermore, in the previous year we have begun
the construction for expanding our guesthouse, which has proved very fruitful in helping us to invite outstanding researchers from overseas. The five additional apartments will be completed by the end of September thisyear. The guesthouse facility is expected to play a much more important role in our collaborative programswith overseas institutes.
We expect that steering the operations of ISM will be very challenging, both in terms of research and also
management in the difficult circumstances in these years. Nevertheless, “statistical mathematics”, which
joins various elements of technology and science and other fields, is indispensable in this “Big Data” Era. We
try to explain the vital importance of ISM’s research work more widely, and we are committed to making a
fruitful contribution to society. We look forward to your wholehearted understanding and support.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on the structural modeling of physical phenomenon related tonumerous factors, and it conducts research on model-based statistical inference methodologies. By means of themodeling of spatially and/or temporally varying phenomena, complex systems, and latent structures, the department
aims to contribute to the development of cross-field modeling intelligence.

■ Spatial and Time Series Modeling Group —————————————————————————————————————The Spatial and Time Series Modeling Group works on the development and evaluation of statistical models, which function effectively interms of predicting phenomena or scientific discoveries, through data analysis and modeling related to space-time-varying phenomena.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies in order to discover the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling.

■ Latent Structure Modeling Group ——————————————————————————————————————————The Latent Structure Modeling Group works on the modeling of variable factors as latent structures existing behindvarious dynamic phenomena in the real world. It also conducts research on methodologies for inference computation
associated with structures on the basis of data related to phenomena.

Department of Data Science
The aim of the Department of Data Science is to contribute to the development of natural and social sciences byconducting research into the methodology of designing statistical data collection systems, measuring and analyzing
complex phenomena for evidence-based sciences, and performing exploratory multivariate data analyses.

■ Data Design Group ——————————————————————————————————————————————————The Data Design Group focuses on research toward designing statistical data collection systems and developing therelated data analysis methods in a variety of survey and experimental environments.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group studies methods for measuring and analyzing complex phenomena to extract statisticalevidence behind them in the various fields of science.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group advances statistical and mathematical research by applying or developing exploratory
multivariate data analyses to clarify latent structures of real phenomena in various fields of both natural and social sciences.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization, and algorithms in statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain phenomena,
stochastic processes and their application to inference, probability and distribution theory, and the related mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data
mathematically and clarify the potential and the limitations of the data theoretically.

■ Computational Inference Group ———————————————————————————————————————————The Computational Inference Group studies mathematical methodologies in the research fields of numerical analysis, optimization,
discrete mathematics, control and systems theory for computation-based statistical inference as well as their applications.

NOE-type Research
Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to uncertainties and risks in society, which haveincreased with the growing globalization of society and economy. Our research projects are mainly seismology, finance,
resources, environmentology, medical-health science, database development and risk mathematics. The Center also
manages a network organization for risk analysis with the goal of contributing to creating a safe and resilient society.
2Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict
the future and to produce designs for effective observation systems by means of “data assimilation”, which is afundamental technology integrating numerical simulations and observational data.

Survey Science Center
Founded on the accomplishments in social research by the Institute of Statistical Mathematics spanning over halfa century including the Study of the Japanese National Character and the cross-national comparative research on
national characteristics, the Survey Science Center was established in January of 2011 in order to facilitate further
growth of the aforementioned sets of research as well as the establishment of networking ties with both domestic
and international research organizations and the increase in the capacity to make contributions to wider society bycreating what we call the NOE (Network Of Excellence).

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data.
This field is based on both the statistical science concerning inference from data and computer science concerning
efficient algorithms. It can be applied to broad disciplines ranging engineering and information science to naturalscience, such as robotics and brain sciences. Our research center aims at supporting the academic community ofthis field, as well as producing influential research through various joint projects.

Service Science Research Center
The aim of the Service Science Research Center is to apply data-centric methodologies to the service fields — frommarketing, supply chain management, and management engineering, to the modeling of social systems. We integrate
the insights from these fields to establish data-centric service sciences as a common discipline, through collaborations
with universities and institutions worldwide, taking advantage of ISM’s Network Of Excellence (NOE) program.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking,from extension courses to a professional development program. The researchers affiliated with the school are often
involved with specific data analysis projects, which help them to gain hands-on knowledge of data science. We
expect such an experienced researcher will play an active role as a number-cruncher, as a modeler, or as a project
coordinator.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways:
managing computer systems and networks, editing and publishing journals, maintaining the library, and manage
extension courses.

■ Computing Facilities Unit ———————————————————————————————————————————————The Computing Facilities Unit is in charge of managing computer facilities, scientific software, networking infrastructure,and network security.

■ Information Resources Unit —————————————————————————————————————————————The Information Resources Unit is responsible for maintaining a library and an electronic repository, and is in charge ofplanning statistical education courses open to the public.

■ Media Development Unit ———————————————————————————————————————————————The Media Development Unit is in charge of publishing and editing of research results and PR brochures.

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Departments and Research Centers
Department of Statistical Modeling

Applied Statistics in Life Science
Our group is promoting the use of Bayesian statistics
and machine learning to address a wide variety of problems
in life science. In below, three of the primary focus areasof our research are introduced.

N Data science-enabled computational

molecular design

The chemical space subject to pharmaceutical developments consists of 10 60 pharmaceutical candidates. Our challenge is to discover new compounds in the huge space thatexhibit desired properties on various pharmacological activities. The aim of this study is to create a new molecular
design method by the integration of Bayesian statistics,
machine learning and quantum chemistry techniques. Our
approach is as follows: (a) we develop forward prediction
models based with experimental data that predict theproperties of compounds from their chemical structure,(ii) the backward prediction is derived by inverting theforward prediction according to the Bayes formula, and (iii)the backward model is used to generate new compoundswith the chemical structures likely to achieve desired
pharmacological activities. Under industry-academia partnerships, we are putting into practice the molecular design
method in the area of resin and pigment chemistry in addition to pharmaceutical developments.

N Understanding of whole neural activities of

C. elegans with bioimage informatics

The nervous system of living organisms realizes advanc­ed and robust information processing in response to di-verse input signals. In neuroscience, C. elegans has been
extensively used as a well-established model organism.
The neural circuit of C. elegans consists of 302 neurons andnearly 7,000 synapses, and their synaptic connections havebeen fully discovered. We are developing a live-cell imaging
technique to simultaneously measure the activity states of
the whole neurons. We use a confocal microscope to measure the spatiotemporal dynamics of the calcium ion concentrations in nerve cells. Bayesian methods play the essential
role to quantify and visualize the neural activities from suchtime-lapse image sequences. This study is supported by
JST-CREST ‘Elucidation of operating characteristics of theneural circuit based on observational data of the whole nervous system’ (Director: Yuichi Iino, The University of Tokyo).

N Omics science with Bayesian methods
The spread of next-generation sequencer technology
has created an explosion in the quantity of genomic data
to analyze, and this in turn has necessitated a radical rethinking of the theoretical methodology for analyzing
such data. Our challenge is to create innovating technologies of omics data analysis, specifically aimed for addressing the following subjects: (a) the development of awhole-genome map of RNA polymerase II transcription
elongation rates, and the elucidation of the biologicalmechanism of the transcription elongation process in cotranscriptional RNA splicing, and (b) discovery of cancerspecific transcription regulatory pathways by analyzing
genome-wide transcription factor ChIP studies.

Ryo Yoshida

Figure 1: Three of the primary focus areas of our research: (A) development of bioimage informatics techniques for neuroscience,(B) omics sciences based on Bayesian statistics, (C) development of computational molecular design method.

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Department of Data Science

Statistical Mathematics and Data Science for Developments ofAdvanced Medical Researches
N Requirements of professionals for statisticalsciences in medical researches
Due to the developments of evidence-based medicine since 1990s, it has been widely recognized thatparticipations of professionals of statistical sciences
are essential for medical researches. Due to the Recent discussions for proper managements of clinical researches in Japan, the government requires advanced
research universities to set up positions of biostatistics professionals, and the role of the Institute of Statistical Mathematics has been increasingly important.
We have conducted many collaborative studies withmedical researchers in the advanced research universities, e.g., basic science studies for ES and iPS cells,
clinical trials for developments of novel drugs, and epidemiologic investigations for PM 2.5 and yellow dusts.
N Network meta-analysis: advanced synthesis

methodology for clinical research evidences
Arrival of the population ageing era and the increase
in medical expenses have been serious problems
throughout the world. In modern medical researches,
we should create reliable evidence for medical technology from multifaceted viewpoints involving comparative effectiveness. Network meta-analysis is a novel

Figure 1: Network meta-analysis of bipolar disorders. Estimatesand 95% confidence intervals for relative risks are presented, which the baseline treatment is placebo ( Miuraet al., Lancet Psychiatry 2014, 1: 351-9 ).

methodology for synthesizing past available evidences of published clinical trials and evaluating the comparative effectiveness of multiple available treatments.
Because of complexities of these data, analysis of the
network meta-analysis requires advanced statisticaland computational techniques, e.g., hierarchical Bayesian modeling and the Markov Chain Monte Carlo. We
have studied these advanced statistical methodologies, and have participated several international projects to generate novel evidence.
N Developments of advanced methodology forbiostatistical science
Advances of technologies supporting for developments of medical research have increasingly progress­
ed in the few decades, e.g., appearance of big data inhealth sciences, clinical trials for developments of advanced medicine, and prediction of treatment effect forindividualized medicine. In the scientific evaluationsof these technologies, a number of problems for analyzing and interpreting data have appeared. Therefore, novel and effective solutions have been required
for statistics and data science. We currently conduct
theoretical and methodological researches for thesechallenging problems, and educational activities tospread these novel methodologies. We consider itshould be a relevant mission for academic researchers
to contribute medicine and society through these activities.
Hisashi Noma

Figure 2: Results of microarray experiments comparing gene expression patterns between ES and iPS cells ( KoyanagiAoi et al., PNAS 2013, 110: 20569–74 ).

5Department of Mathematical Analysis and Statistical Inference

Statistical Analysis Using Curvature of Data Spaces
N Improving statistical analysis by tuningcurvature of data spaces
When the data is distributed not on the whole
space but only on a subset, called a data space, we
can improve accuracy of statistical analysis by estimating the data space. In our study, we tune metricand curvature of the estimated data space and propose new statistics based on the data space. It has
been known that if the curvature of a data space is
non-positive, the intrinsic means, a generalizationof ordinary average in Euclidean space to in general
geodesic spaces, exist uniquely. We further change
the curvature of the data space and control thenumber of local intrinsic means: multiple intrinsic
means are sometime more welcome, for example,
for clustering. However, in general the data space ofreal data is much more complicated than simple
shapes being studied in theory. To solve this serious difficulty, we proposed a method using an empirical graph as an approximation of the original
data space and changing the curvature by rescaling
the edge lengths exponentially. We verified our
methods can improve existing statistical methods
for various kinds of real data including climate data.

the tree space in phylogenetic tree analysis was a
breakthrough since it has a good geometrical property called CAT(0) and linear methods like hypothesis testing and PCA can be applied. In this project,
we search how this method can be applied to data of
more general structures. For example, dendrograms
are a special case of tree graphs and we can prove
CAT(0) property for such a case, too. By using thistheoretical fact, we proposed a permutation test byusing geodesic metric on a space of dendrograms.
We are now trying to generalize these theory andmethods to more general graphs and complexes.
Kei Kobayashi

N Data spaces of graphs and statistical analysis

using their CAT( 0 ) property
The mean (middle point) of two points at the North
Pole and South Pole of a sphere in a general sense isat the center and no more on a sphere. This can happen for most of the statistical analysis since their
computation is usually linear. Meanwhile study of

Figure 1: Uniqueness of the intrinsic means depends on curvature of the data space. For example on the sphere,
whose curvature is one, the intrinsic means of two
data at the North and South Poles are all points on
the equator.

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Figure 2: The number of edges used in some geodesics varies
when we change the edge lengths exponentially. The
curvature of data space can be controlled by this.

Figure 3: Data spaces of tree graphs are CAT(0) and have similar
properties to spaces with non-positive curvature. We
aim to generalize theory and method for such spaces.

Departments and Research Centers
Risk Analysis Research Center

Exploring Efficient and Effective Management Approaches
to Mitigate Risk by Disturbance
N Optimization model for forest management

planning“When, where and how much to harvest” has been a
primary concern for resource managers for many years.
In order to assist such decisions, considerable effort
has been given to develop optimization models searching for the optimal management strategy, which maximizes the amount of harvest from resources or profit
induced from such harvests subject to given constraints.
Integer programming allows us to formulate the constraints necessary to avoid resource degradation due
to unplanned harvesting activities. For example, harvested units can be dispersed over space and time to
avoid the creation of large openings, simply by introducing adjacent constraints that prohibit harvesting
adjacent management units in the same period.

on the spatial information of each management unit.
Not only can GIS be use to archive spatial information,
but it can also be used efficiently to generate adjacency relationships among management units. GIS can beused to visualize the optimal spatial harvest patterngenerated from an optimization model (Fig.2).
N Mitigating the risk surrounding forest resources

Figure 1: Adjacency relationships among management units.

with optimization model
As a result of global climate change, forest managers
are now faced with the challenge of how to mitigate thedamage caused by disturbance agents such as forest
fires, insects attack, and invasive species. Damages
caused by disturbance agents are spatial in nature, forexample, fire spreads from an ignition location overspace and time. Spatial patterns of forest vegetation
affect the degree and extent of forest damages causedby these disturbance agents. Fire for example, spreads
rapidly when it enters a contiguous area where fuelhas been accumulated. Forest managers can mitigate
the damage caused by disturbance agents simply bycontrolling the spatial distribution of forest vegetation
which takes into account the expected spatial damage
patterns. Optimization models can be used to explore
the optimal spatial patterns that mitigate the damages
caused by disturbance agents. The results presented
in Fig 3 were produced by integrating a simulation model that projects spatial damage patterns into an optimization model to search for the efficient and effective
forest management allocation patterns for mitigatingthese kinds of risk.
Atsushi Yoshimoto

Figure 2: Visualizing optimal spatial harvest pattern with GIS.

Figure 3: The optimal management pattern for mitigating
the risk of damage from invasive species.

N Use of Geographic Information System (GIS)

In order to consider spatial distribution of resource
harvest units, it is important to figure out adjacency
relationships (Fig. 1) among management units based

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Research and Development Center for Data Assimilation

Modeling of Space Debris Generated by Breakup Events
N The problem of space debris

Artificial objects in space, which are not operational, are called as space debris. For example, abandoned satellites, rocket bodies, fragments generated by explosions and collisions and slag of solid
rocket motors are included. The velocity of object
in orbit is about 7 km, therefore the collisions leads tothe malfunction or destruction of operational satellites even if the collided object is small. Moreover,the fragments generated by collisions induce more
collisions. On February 2009, Operational IRIDIUM
33 collided with abandoned COSMOS 2251. This
breakup event generated a lot of fragments.
N Fragments generated by breakup events

Since the launch of SPUTNIK 1, the number of artificial objects in space is increasing. Especially, there
are a lot of fragments generated by explosion and collisions. Therefore, it is important for evaluating therisk and mitigation measures to model the fragments
generated by breakup events. However, the small
fragments cannot be observed from the Earth because of the limit of observation instruments. From

Figure 1: Outline of this research8

the view of this fact, modeling of fragments generated by breakup helps to understand the current spacedebris population.
N Modeling of space debris

NASA’s standard breakup model is usually used
for modeling of fragments generated by breakup
events in predicting the future space debris population. However, this model does not formulate the distribution of the direction of ejecting fragments. Witha focus on this point, I am engaging in modeling of
the fragments generated by a breakup event using
the measurements provided by the USA and data
assimilation technique.
Yuya Ariyoshi

Departments and Research Centers
Survey Science Center

Survey on Language Standardization in Tsuruoka Cityin Cooperation with NINJAL
N What is the Survey on Language

Standardization in Tsuruoka?

Up to the present time, the National Institute for Japanese Language and Linguistics (NINJAL) in Tachikawa, Tokyo, has conducted the continuous Survey on Language
Standardization in Tsuruoka City, Yamagata Prefecture,(hereafter SLST) on four occasions. The first survey was
conducted 65 years ago in 1950, the second in 1971, thethird in 1991, and the fourth in 2011, meaning it has been
conducted continuously at intervals of approximately 20
years.
The survey is conducted as joint research, and the Institute of Statistical Mathematics closely cooperated withNINJAL for the first and second SLSTs at each of the stages of survey planning, implementation, and data analysis.
As part of a series of projects to create Networks Of Excellence (NOE), the Institute of Statistical Mathematics Survey Science Center and NINJAL conducted the fourth survey as a cooperative training survey.

The objective of the SLST is to utilize survey items on dialect and standard language to clarify one aspect of the realities of people’s language life in Japan. The SLST as a wholeconsists of a repeated cross-sectional random sampling(RS) survey and a longitudinal panel (P) survey. The RS survey is intended to ascertain changes in society as a whole,and the P survey changes to individuals’ language life.

N Changes in the standard language rate
As an example, Figure 1 shows the changes in the standard language rate of the spoken item ‘bell’ (in Japanesesuzu). The survey participants were shown a picture of abell, were recorded uttering it, and the rate of utterancesthat were considered standard was calculated and plotted.
In the figure to the left, which shows age groups on thehorizontal axis, a trend can be observed of the standard
language rate rising as a whole over the passage of eachtime period, with the tendency being that the higher theage group, the lower is the standard language rate (As of
2011, when the fourth survey was conducted, almost all
respondents used standard language, excluding some elderly people.). Although the data are identical, in the figure to the right, which shows the birth year on the horizontal axis, the plot of the standard language rate can beseen as a connection of lines, from which it is understoodthat standard language use has increased with each new
generation.

N Separation of the effects of age group,

time period, and generation; a cohort analysis

Figure 1: Changes in the standard language rate of the spoken
item “211 ‘bell’ suzu” (1st to 4th Tsuruoka RS Survey),
totals for males and females, left: according to age
group, right: according to birth cohort.

When data like those in Figure 1 are obtained from repeated surveys, it is possible to apply the cohort analysis
method that separates the effects of age groups, time periods, and birth cohorts (a cohort is a group born at thesame time = a generation). Figure 2 shows the results ofthe cohort analysis on ’bell’ suzu. The cohort effects in the
figure furthest to the right support the existence of generational differences, as it can be seen to connect with theplot according to birth year in theright-hand figure of Figure 1. The
time-period effects in the furthest
figure to the left clarify that there
are changes to society as a whole,regardless of age group or generation. The age-group effects, shownin the central figure, capture thechanges in the aging of individuals, which are not easily seen.

Takashi Nakamura
Figure 2: Results of the cohort analysis on the standard language rate of the spoken item
“211 ‘bell’ suzu” ( 1st to 4th Tsuruoka RS Survey ), totals for males and females.

9Research Center for Statistical Machine Learning

Inference of Uncertain Systems— Fusion of Kernel Methods and Sampling —N System with uncertainty

There are two major methodologies for systemmodeling: parametric and nonparametric inference.
The former assumes the system can be described
well by a function class with finite parameters, andestimates them; the latter constructs a more andmore flexible function as the sample size increases,
without restricting the degree of freedom. In modern
data analysis, by the advance of information andsensor technologies, we need modeling of complex
systems, which often involve parts described wellwith a parametric model and parts that do not allow
simple parametric modeling but require data-driven
nonparametric modeling.
N State-space model

To illustrate such situations, consider a visionbased robot localization problem, where we wish to
estimate the location of a robot in a fixed environment

such as a building. Since the robot follows a mechanical dynamics, we can basically estimate the current
location and orientation given location, orientation,and motor input in the previous time step. More accurate estimation is possible, however, if we can calibrate it based on sensor information such as vision
images of the robot. The problem is thus how to effectively combine the state-transition rule and thesensor information. It is known that this type of inference problem can be formulated and inferred withthe general framework of state-space models. Difficulty of this robot-localization problem lies in thecomplex measurement model: the relation between
the images and the location-orientation of a robot isvery difficult to describe with a parametric model
because of its high-dimensionality and dependence
on environment. With the lack of explicit functional
form, it is not possible to apply the standard Kalmanfilters or particle filters.
N Fusion of kernel methods and sampling

Figure 1: Robot localization

Figure 2: Kernel Monte Carlo Filter10

Our research group has developed the KernelMonte Carlo Filter, which is a new approach combining kernel methods and Monte Carlo sampling forsolving the above inference problem. For inference
involving the uncertain measurement model, we apply the data-driven nonparametric method, Kernel
Bayes’ Rule, which has been proposed by our group,
while the known state-transition is utilized by Monte
Carlo sampling. In application to the robot-localization
problem, the proposed method shows significant improvement of estimation accuracy in comparison withother existing methods which
are applicable to this problem
setting.
This work has been done ininternational collaboration with
Prof. Arthur Gretton, Gatsby
Neuroscience Unit, University
College London. We are aiming
at extending our method to inference on systems of further
uncertainty.
Kenji Fukumizu

Departments and Research Centers
Service Science Research Center

Privacy-preserving Publishing of Pseudonym-based Trajectory
Location Data Set
N Overview

Nowadays a huge number of people are using mobile devices equipped with a GPS receiver, and so it
has become feasible to keep track of people’s movements over a wide area by collecting GPS data fromthose mobile devices. Such a large volume of location
data gives us a precise global view of people’s mobility patterns, and we can thus support analytic location-based services, such as real-time traffic monitoring and urban planning for future sustainable cities.
However, due to the significant concern about location privacy, the sharing of mobile users’ location traces has largely been restricted to anonymized data
sets where users’ identities are removed. We usuallyneed to follow the practice of ensuring k-anonymity,
which degrades the granularity of location data to ensure that every location contains more than k people.

Consequently, k anonymized data sets provide little
information on users’ mobility patterns, which makes
it difficult to link multiple data points produced by thesame user.
We, therefore, propose a new dynamic pseudonym
scheme for constructing a location data set that retains users’ path information while preserving theirlocation privacy. Our basic approach is to exchange
multiple users’ pseudonyms only when they meet atthe same location to eliminate the linkability of theirpseudonyms before and after that exchange. We believe that such a dynamic pseudonym approach iseffective since many people move through hub locations (e.g., a train station near sightseeing spots)
where many people meet, as shown in Figure 1. Our
privacy metrics in Figure 2 requires that, at a given
time t, every user has a sufficient number of plausible paths heading towards k different locations.
N Experimental results

Figure 1: Concept of the mix zone

Our pseudonymization technique essentially divides a user’s whole trajectory path into
multiple segments and thus degrades the utility of the dataset. We thus conduct experimentswith the real location dataset containing mobility traces of taxi cabs in San Francisco, USA andquantitatively evaluate tradeoffs between users’ privacy and data utility. Our initial resultsin Figure 3 show that it is possible to achieve
high data utility in pseudonymized datasets
while satisfying realistic privacy requirements.
Kazuhiro Minami

Figure 2: Concept of pseudonym-based location privacy. Y-axis
represents indexes to pseudonyms taken by the user.
Each node in the graph represents a mix zone where
the user meets some other users.

Figure 3: Tradeoff between privacy and data utility.

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NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
N Research and educational activities by

Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues
research and education along the two lines of basic research, NOE (Network Of Excellence)-type research (wewill explain about “NOE” later) and professional development. These are respectively conducted by the basic
research departments along a horizontal axis, and theNOE-type research centers and the school for professional development along a vertical axis (Figure 1).
By its nature, the basic research departments (along
the horizontal axis) cut across and link various disciplines, developing tools for interdisciplinary research.
The field of statistical mathematics must itself evolve tomeet the changing needs of society and the data environment, and is therefore constantly evolving as a fieldof study. At the same time, there are approaches anddirections that remain unchanged as the field evolves.
For that reason, we have chosen not to call it fundamental
research or foundational research but “basic research”,
to reflect both the fixed and evolving qualities of statistical mathematics. There are three basic research departments: Statistical Modeling, Data Science, andMathematical Analysis and Statistical Inference. These
departments engage in cutting-edge research to develop
methodologies for rational prediction and decision making, based on data and existing knowledge. All professors
and associate professors in the Institute are assigned to
one of these basic research departments.
On the other hand, the NOE-type research centers
and the school for professional development (along thevertical axis) are staffed by permanent researchers within the Institute, project professors/researchers (postdoctoral staff), and visiting professors and researchers.

Figure 1: Biaxial Structure for research and education

12

The current NOE-type research centers are “Risk
Analysis Research Center”, “Research and Development
Center for Data Assimilation”, “Survey Science Center”,“Research Center for Statistical Machine Learning”, and“Service Science Research Center”. These centers conduct research activities that interface statistical mathematics with individual scientific disciplines in order tofind solutions to urgent social problems.
We set “School of Statistical Thinking” as the schoolfor professional development, which also serves multiple
programs of the project for fostering and promoting statistical thinking. In School of Statistical Thinking, researchers, students, and contract researchers from private companies who have an eye on the creation of a newstatistical research field and various people who realize
the necessity of statistics in their particular research
field train together to foster statistical thinking. Of special importance, young project researchers receive onthe-job training from their senior mentors and thereby
obtain the assorted skills of “statistical thinking”. In consideration of the importance of “statistical thinking”, wehave changed the personnel system to post newly employed assistant professors at School of StatisticalThinking since the 2013-2014 Japanese academic year.

N NOE ( Network Of Excellence) ProjectIn accordance with the second medium-term plan of
Research Organization of Information and Systems, theInstitute of Statistical Mathematics has set as a goal theestablishment of NOE (Network Of Excellence) in statistical mathematics. With the formation of NOE in each of
five research fields —“Risk Research”, “Next-Generation
Simulation”, “Survey Science”, “Statistical Machine Learning”, and “Service Science”— we are conducting joint

Professor Emeritus, Tokyo Instituteof Technology

Dr. Takatoshi Imai

Adjunct Professor, Computational
Science Education Center, Kobe
University

Dr. Yoshio Oyanagi

President, The Japan
Pharmaceutical Manufacturers
Association

Mr. Masayo Tada

President, Toyota Technological
Institute at Chicago

Dr. Sadaoki Furui

Director-General, Center for Research
Development and Strategy, Japan
Science and Technology Agency

Dr. Hiroyuki Yoshikawa

Director-General, Institute for
Monetary and Economic Studies,Bank of Japan

Mr. Kenichiro Watanabe

Table 1: Members of Advisory Board of “NOE Project”( as of April, 2015 )ISM Projects

research activities that serve to deepen collaborative
relationships with other institutes, both within Japanand overseas. The aim of this project is to establish newscientific methodologies (“Fourth Paradigm”), develop
hubs for cross-disciplinary exchanges, and create newstyles of networked joint research. The aforementioned
five NOE research centers serve as core hubs in each
NOE domain.
To fulfill the goal of establishing the new scientificmethodologies (“Fourth Paradigm”) in a knowledgebased society, in which the importance of knowledge
goes beyond merely solving individual problems, NOE
activities are being systematically pursued through thecoordination and support efforts of NOE Promotion Unit,
under the unified project guidelines decided by Managing Committee of NOE Project. At the same time, advice
is also taken from the members of Advisory Board of
NOE Project (see Table 1), made up of experts from theindustrial, academic, and government sectors.
NOE Project, which is made possible by ISM’s specialist focus in the cross-disciplinary field of “statisticalmathematics”, is attracting strong support from each ofthese communities. On the basis of this project, ISM, asan Inter-University Research Institute, will be providing
further opportunities to the industrial, academic, andgovernment communities, for joint usage (of facilities)
and joint research.

N Expansion of NOE activities
As described above, the five NOE research centersserve as core hubs for their respective fields. ISM is promoting the signing of MOUs (Memorandums of Understanding) with research organizations within Japan andoverseas, and so these NOE research centers are planning for the expansion of their NOE activities. Since
2013, MOUs spanning a variety of NOE research fields
have gone into effect. On top of this, our exchanges withorganizations with which MOUs have been in effect for
longer have grown stronger, resulting in concrete collaborative activities. It is clear that NOE Project is establishing much deeper roots within the research community.
With its focus on methodology, ISM continues to plan
research in each of these fields: Risk Research, NextGeneration Simulation, Survey Science, Statistical Machine Learning, and Service Science. With all research
activities concentrated under NOE Research Promotion
Organization, the general body that oversees the fiveNOEs, ISM is expanding and developing its NOE projects
with the aim of establishing new scientific methodologies(“Fourth Paradigm”), creating new research disciplines,
and developing new styles of joint research. Figure 2
shows a diagram of NOE Project. For a more detailed outline, please visit our website.

http://www.ism.ac.jp/noe/en/project/

Figure 2: Relationship Diagram of NOE ( Network Of Excellence )13

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data.
Now surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is aneed for data scientists who can handle such big data and are able to extract useful knowledge from it.
Meanwhile, Japanese higher education is exhibiting a deplorable lack of production capacity in terms ofdata scientists. This can be accounted for by the fact that no academic institution other than ISM has adepartment of statistics and the small number of statisticians in academia are isolated from each other,
being scattered over various disciplines. Hoping to gain a little traction on this problem, ISM establishedthe School of Statistical Thinking, into which we integrated all of our educational resources. The following
are the principal projects.

Research Collaboration Start-up
The Institute had been providing a consultation
service on statistical science. Along with the launchof the School of Statistical Thinking in November
2011, this service was reorganized as a researchcollaboration start-up. This program, being one ofthe projects to foster and promote statisticalthinking, is mainly aimed at supporting appliedscientists and other related people. Expert statisticians affiliated with the Institute give scientists
and others advice on statistical modeling, data
analysis, and research. Some cases develop into
official research collaborations, which are our primary duty as an inter-university research institute. The start-up program covers a variety of topics, ranging from introductory to more specializedstudies. Half of the clients are from the private sector, and the rest are staff at public organizations,

university professors, or students. The Instituteaccepts about 40 cases annually, some of which
benefit society in diverse ways.

Open-type Human Resource Development Program
In order to foster and promote “statistical thinking”, the Institute of Statistical Mathematics invites
proposals of research projects and workshops todevelop young researchers. In FY 2015, four workshops have been adopted after review.

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars are led by in-house and external lecturers to
showcase their latest studies. These lectures are
14

free to attend. To view the seminar schedule andlearn more about the program, please visit the Institute of Statistical Mathematics website.
http://www.ism.ac.jp/index_e.html

ISM Projects

Open Lecture
We hold an open lecture during Education andCulture Week every year (November 1 through7), to introduce the Institute’s activities and topromote statistical science. We invite lecturers
to speak on a timely topic relating to statisticalscience. The lecture is open to the general public.
For further information, please visit the website of
the Institute of Statistical Mathematics.
http://www.ism.ac.jp/kouenkai/

Data Science Research Plaza
Researchers from private-sector firms can havea desk and phone inside the School of StatisticalThinking. This program is subject to fees, and thecontract can be renewed annually. Visitors canfreely attend various events, such as seminars,
workshops, conferences, and extension courses.

Tutorial Courses
The education program at ISM dates back to
1944, the year of founding. The Ministry of Education installed a training center within ISM to foster
technicians in numerical computation. After theWorld War II, this training center was relaunched
in 1947 to develop pollsters and census takers. It
helped to cultivate professionals in the field of statistical surveys, while a growing number of entries

from business and industry coming for various
types of training were also observed.
When ISM joined the Inter-University ResearchInstitute Corporation in 1985, the training center
was formally shut down. We nevertheless tried tomaintain 4 courses per year, relying on support
from the private sector. The number of courses
was tripled when ISM became an independentagency in 2005. Now the tutorial courses are operated by the School of Statistical Thinking, which
was established in 2011.
In the 2014 academic year, 13 courses were held
and the number of participants was 776. The total
number of courses held from 1969 to March, 2015
was 321, with a total of 23,046 participants. These
courses covered a wide range of fields from basic
to applied statistics. The schedule of tutorial
courses can be found on the website of theInstitute of Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html

15

Coop with Math Program

ISM Projects

Towards the Formation of Research Foundations for Collaboration
between Mathematics, Various Scientific Fields, and Industry
N What is the Coop with Math Program?

The Program mainly consists of supporting andorganizing workshops, study groups, working groups,
and outreach activities for the general public.
Workshops aim to discover new or challenging
problems which it is hoped will be solved by the collaboration of industries and various scientific fields
with mathematical science.
Study groups, which originally started at Oxford
University in 1968, differ from workshops. Generally, coordinators first provide several problems andeach participant chooses one of these and concentrates on solving it for about a week. At the end of astudy group, participants who worked on the sameproblem together give presentations about solutions or proposals for solutions to the problem.
In fiscal year 2014, the Program conducted 21

workshops and 9 study groups focusing on various
areas. Remarkably, more than half the problems instudy groups come from industries.
The Program independently supports finding
problems in specific fields which are expected to besolved by mathematical science through organizing
tutorial seminars and workshops, which include theactivities of working groups. In fiscal year 2014, wehave produced a proposal for collaborations between mathematical science and life science. We
have also begun to organize a community in which all
researchers can easily access activities not only ofmathematicians but also of materials scientists, sothat research via collaborations between mathematics and materials sciences is promoted.
The Program organizes various outreach activities not only for researchers but also for the generalpublic. For example, we have organized talk-exhibition events at Odaiba (2013, 2014), which helped
people feel more familiar with mathematical sciences. The Program also supports career-path seminars so that students, and academic and industrial
researchers can exchange their thoughts in detail.
By continuing these activities, we hope that mutual comprehension and collaborations between researchers in mathematics and other fields will beenhanced as much as possible.
Information about the Program can be accessed
through its website (http://coop-math.ism.ac.jp),
which consists of the original application system,
SNSs such as Facebook (https://www.facebook.
com/CoopMath) and Twitter (@CoopMath), and ourmail magazine. To learn more about the Program,please visit these websites.

Figure 1: Discussion and Experiment in Study Group

Figure 2: Demonstration of Robots in Science Agora

The Coop with Math Program is a MEXT funded
project which was originally planned in 2012 in an
atmosphere of promoting research via collaborations between mathematics and other scientificfields, as well as industries. The aim of this programis to provide mathematicians and researchers invarious scientific fields and industries with opportunities for inter-field discussions, and thus, to promote the finding of solutions to challenging problems via collaborations. It was started as a five-year
project and commissioned to The Institute of Statistical Mathematics. The Institute currently manages
the following activities under the Program with theaid of eight cooperating institutions along withmain themes decided by the steering committee.

N Activities of the Coop with Math Program16

Data Scientist Training Network

ISM Projects

How to Develop Data Analytics Talents
N Developing talents for big data

N Current status of “Data Scientists” in Japan

Data analytics is quickly becoming an essential
tool to make business decisions in various industries, such as retail, distribution, medical, education, and public sectors. One of the major obstacles
of applying data analytics is the lack of talents. To
address this issue, The Ministry of Education, Culture, Sports, Science & Technology in Japan (MEXT)
launched a three-year project for developing data
analytics talents and making the best use of them.
The ISM is leading this project with the help of The
Graduate School of Information Science and Technology, The University of Tokyo. The goal is to identify the model of talent development and their career that are best suited in Japan, and to share themodel among the stakeholders such as academia,
industry, and the governments as well as NGO’s.

In the first year of the project, we conducted astudy on the current status of “data scientists” inJapan. The study consists of a) a survey on those
who passed certain levels of the certification testsof Japan Statistical Society Certificate (319 valid responses) and b) a series of interviews with 20 wellknown data scientists in Japan. The results indicatethat typical data scientist in the United States andthose in Japan are somewhat different: in the US,data scientists are more product-focused, meaningthat they develop working systems with embedded
data analytics, while in Japan, data scientists are
typically service professionals who help analyzing
their clients’ data.

N Training materials and their deployment
Based on the findings acquired in the firstyear of the project, we have identified skills
needed in these data scientists and started
to develop a program for them. The program
includes a) an online training material, titled“Data Scientist Crash Course,” that teaches
basic data analytics concepts in a very concise manner, and b) an internship programthat presents an opportunity for students
who want to be a data scientist to get firsthand experiences of analyzing data. We also
compiled approximately 200 data scientist
training courses, both free and with a charge,
into a database.

Figure 1: Joint session on the internship program

Figure 2: Data Scientist Crash Course

17Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census
Stichting Mathematisch Centrum
Institute for Statistics and Econometrics, Humboldt University of Berlin
Institute of Statistical Science, Academia Sinica
The Steklov Mathematical InstituteCentral South University
Soongsil University
Department of Statistics, University of Warwick
The Indian Statistical InstituteDepartment of Empirical Inference, Max Planck Institute for Biological Cybernetics
Faculudade de Medicina da Universidade de São Paulo
Department of Communication Systems,SINTEF Information and Communication Technology
Human Language Technology Department, Institute for Infocomm ResearchCentre for Computational Statistics and Machine Learning, University College London
Department of Electronics and Telecommunications, Norwegian University of Science and Technology
Department of Probability and Mathematical Statistics, Charles University in Prague
The Department of Ecoinformatics, Biometrics and Forest Growth of the Georg-August University of Goettingen
The Korean Statistical Society
Toyota Technological Institute at Chicago
Mathematical Sciences Institute Australian National University
RiskLab ETH Zurich
Institut de Recherche en Composants logiciel et materiel pour l’Information et la Communication Avancee (IRCICA)
Le laboratoire de mathematiques de I’Universite Blaise Pascal
Centre de Rechereche en Informatique, Signal et Automatique de Lille (CRIStAL) UMR CNRS 9189
University College London (UCL) Big Data Institute
The Institute of Forestry, Pokhara of Tribhuvan University
The Institute of Forest and Wildlife Research and Development of the Forestry Administration of Cambodia
The Chancellor masters and Scholars of the University of Oxford

USA ( Washington)

July 27, 1988
May 10, 1989
December 8, 2004
June 30, 2005
August 9, 2005
November 18, 2005
April 27, 2006
January 16, 2007
October 11, 2007
August 11, 2010
April 15, 2011
January 30, 2012
February 16, 2012
February 16, 2012
May 22, 2012
October 10, 2012
October 18, 2012
July 9, 2013
February 10, 2014
May 15, 2014
February 7, 2015
February 9, 2015
February 11, 2015
February 12, 2015
February 26, 2015
March 6, 2015
March 6, 2015
March 10, 2015

The Kingdom of the Netherlands (Amsterdam)

Germany (Berlin)
Taiwan ( Taipei)
Russia (Moscow)
China (Changsha)
The Republic of Korea (Seoul)
the United Kingdom (Coventry)
India (Kolkata)
Germany ( Tubingen)
Brasil (Sao Paulo)
Norway ( Trondheim)
Singapore (Singapore)
the United Kingdom (London)
Norway (Trondheim)
Czech Republic (Prague)
Germany (Goettingen)
The Republic of Korea (Seoul)
USA (Chicago)
Australia (Canberra)
Switzerland (Zurich)France (Paris)
France (Clermont-Ferrand )France (Paris)
The United Kingdom (London)
Nepal (Pokhara)
Cambodia (Phnom Penh)
The United Kingdom (Oxford)

Research Collaboration
This academic study program provides research­ers from other academic institutes with access to the facilitiesof the Institute, and provides opportunities for researchers to conduct theoretical and applied studies on statistics.

■ Number of Activities

2009
154

2010
135

2011
172

2012
182

2013
181

2014
177

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to findthe most appropriate type of project.
ISM Fields
Numberabc

FieldsNumberSpatial and Time Series
Modeling Group
Complex System Modeling Group
Latent Structure Modeling Group

d
e
f
g

FieldsNumberData Design Group
Metric Science Group
Structure Exploration Group
Mathematical Statistics GrouphijFieldsLearning and Inference Group
Computational Inference Group
OthersMajor Research Fields
NumberFieldsMajor Research Domains

4

Statisticalmathematics
Information
science
BiologicalsciencePhysicalscience5Engineering

Mathematical theory of statistics,
optimization, etc.
Algorithms, use of computerin statistics, etc.
Medicine, pharmacy, epidemiology,
genetics, etc.
Space, planet, earth, polar region,materials, etc.
Mechanics, electronics, control,chemistry, architecture, etc.

1
2
3

18

NumberFieldsMajor Research Domains

6
7

Human
science
Socialscience

Philosophy, art, psychology, education,
history, geography, culture, language, etc.
Economics, law, politics, society, manage­
ment, official statistics, population, etc.

8

Environmental
science

Environmental Statistics, Environmetrics,
Agricultural Statistics, Land Economics,
Landscape Management, Forest
Management, etc.

9

OthersOther research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (the Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

President

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The course includes designing of data-gathering systems, modeling, inference and forecasting in order toextract information and knowledge from the real world based on empirical data, as well as basic, mathematicaland applied education and research related to these fields. The course aims to provide the student with skills
that help to contribute to solving important and connected issues and give the ability to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

19

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science by professors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well
as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of thebest in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible forstudents to develop their own projects by participating in research projects with other institutions throughthe Transdisciplinary Research Integration Center, Research Organization of Information and Systems.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits, including the required ones, by a three-year doctorate student who previously
completed a Master’s course; meeting all the criteria set by the thesis committee of the Institute; and successfully completing the final examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2015 )■ Doctor’s course five years■ Doctor’s course three years

Year of enrollment

2010

2012

2013

2014

Year of enrollment

2008

2011

2012

2013

2014

2015

Number of students

2

2

2

2

Number of students

1

3

3

4

8  (1 )1

* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University (3 ) • Tohoku University ( 3 ) • Fukushima University (1 ) • University of Tsukuba (6) • Saitama University (1 )• Ochanomizu University (1 ) • Hitotsubashi University ( 6 ) • Chiba University (1 ) • The University of Tokyo ( 9 ) • Tokyo Medical and
Dental University (1 ) • Tokyo Gakugei University (2 ) • Tokyo Institute of Technology ( 4 ) • Tokyo University of Marine Science andTechnology (1 ) • Tokyo University of Agriculture and Technology (1 ) • Tokyo Metropolitan University (1 ) • Shizuoka University (1 )• Japan Advanced Institute of Science and Technology, Hokuriku (1 ) • Nagoya University ( 3 ) • Toyohashi University of Technology ( 2 )• Kyoto University ( 4 ) • Osaka University ( 2 ) • Osaka City University (1 ) • Nara Institute of Science and Technology (1 ) • Okayama
University ( 2 ) • Shimane University ( 3 ) • Kyushu University ( 2 ) • Oita University (1 )20Graduate School Program

University Background of Students
Private universities• Okayama University of Science (1 ) • Tokyo University of Science ( 6 ) • Kyoto Sangyo University (1 ) • Keio University ( 8 ) • Waseda
University ( 8 ) •Chuo University ( 7 ) • Toyo University (1 ) • Nihon University ( 2 ) • Hosei University ( 7 ) • Kurume University (1 )• Japan Women’s University (1 ) • Shibaura Institute of Technology (1 ) • Nanzan University (1 ) • Kansai University (1 ) • Kitasato
University (1 ) • Ritsumeikan University (1 )Foreign universities• Aston University (1 ) • University of California, Irvine (1 ) • University of Campinas (1 ) • University of Colorado at Boulder ( 2 )• University of Dhaka ( 2 ) • University of Hawaii (1 ) • Jahangirnagar University ( 2 ) • University of Malaya (1 ) • Northeast Normal
University (1 ) • Ohio University (1 ) • University of Rajshahi ( 2 ) • Stanford University (1 ) • Center for Analysis and Prediction,
China Seismological Bureau (1 ) • Northeastern University of Technology, China (1 ) • The Hong Kong University of Science andTechnology (1 ) • University of Science and Technology of China (1 ) • Chinese Academy of Sciences, Institute of Applied
Mathematics (1 )Degrees Awarded
Year

2009

2010

2011

Doctor of Philosophy

5 [1]

7 [1]

4

2012

2013

2014

6 [1]

6

5

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute ofTechnology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University• Hokkaido University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences• JAXA’s Engineering Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan• Japan Broadcasting Corporation • Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Aichi University of Technology • Tokyo University of Information Science • Shibaura Institute ofTechnology • Rikkyo University • Waseda University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone
Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd. • PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma
Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center21

Facilities and Equipment

Computational Resources
Since July 2014, the ISM has maintained three different
supercomputer systems: the Supercomputer System for
Data Assimilation (nicknamed “A”), the Supercomputer
System for Statistical Science (nicknamed “I”), and theCommunal Cloud Computing System (nicknamed “C”).
Those are nicknamed in tribute to a former director general, the late Professor Hirotugu Akaike, who invented thefamous model selection criterion AIC. As of July 2015, thesystem I has just been expanded to have Haswell nodes.
System A is the world’s largest shared-memory supercomputer system and consists of two sets of SGI UV 2000
(each 256 nodes of 10-core Xeon E5-4650v2, 64 TB memory). Half of this system is taking part in the high-performance computing infrastructure (HPCI) project.
System I is a large distributed-memory supercomputer that consists of 400 nodes of SGI ICE X with Ivy Bridgeand 136 Haswell nodes, having 6,432 cores in total. The
system also includes a large-scale shared storage system(2.5 PB disk storage), physical random number generator boards, and a visualization system that supports a

The Supercomputer System for Data
Assimilation ( nicknamed A )maximum resolution of 4,096 × 2,160 and has a 200-inch
rear projection screen for 3D visualization.
System C consists of 64 Dell PowerEdge R620 (two10-core Xeon E5-2680v2, 256 GB memory). This systemprovides easy-to-use computing environments, such as
distributed-memory statistical computing environments
and Web servers, running on Apache CloudStack software.
In the main office, the primary local area network(LAN) consists of an Ethernet network using 10GBASESR for the main trunk and 1000Base-T for branches. The
personal computers in researchers' offices, and systems
A, I, and C are all connected to this network. A wireless
LAN system is also available in the immediate area of thebuilding occupied by the institute. These LAN systems
enable distributed processing and allow computing resources and statistical data to be used effectively. Comprehensive network security methods have been implemented, such as a firewall system, anti-virus software,and an intrusion prevention system. To encourage joint
research with researchers both in Japan and abroad, as
well as the exchange of e-mails, the network is connectedto the Internet through SINET4 (10 Gbps).

The Supercomputer System for Statistical
Science ( nicknamed I )The Communal Cloud Computing
System ( nicknamed C )Library and Materials ( As of April 1, 2015 )We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

22

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.

Finance and Buildings

Administration Subsidy and Others ( 2014 )Type

Personnel expenses

Non-personnel expenses

Total

663,312958,199

1,621,511

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2014)
Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

19

13

4

36

Income124,156

24,6005,600

154,356
Unit: ¥1,000

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2014 )Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas

2

13,390Grant-in-Aid for Scientific Research ( S )1

15,990Grant-in-Aid for Scientific Research ( A)

3

26,910Grant-in-Aid for Scientific Research ( B)938,350Grant-in-Aid for Scientific Research ( C)

18

25,522Grant-in-Aid for Challenging Exploratory Research

6

7,629

Grant-in-Aid for Young Scientists ( B )

9

7,039

Grant-in-Aid for Research Activity Start-up

1

1,430

Grant-in-Aid for JSPS Fellows

5

5,589

54

141,849

Total

Unit: ¥1,000

Site and Buildings ( As of April 1, 2015 )Site Area

62,450m 2

Area for Buildings (total)

16,026m 2

23

Organization

Organization Diagram ( As of August 1, 2015 )24

Number of Staff ( As of April 1, 2015 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling

7

7

1

15

Department of Data Science

4

8

3

15

Department of Mathematical Analysis and Statistical Inference

7

4

2

13

2

2

School of Statistical Thinking
Center for Engineering and Technical Support

1010

Administration Planning and Coordination Section

1

NIPR/ISM Joint Administration Office

12 ( 27)

1 ( 2)

13 ( 29)

13 ( 27)

11 ( 2 )

70 ( 29)

Total

1

18

19
(

8

1

) Total number of staff of NIPR/ISM Joint Administration Office.

The number under Technical Staff at the Center for Engineering and Technical Support includes
one staff member who retired because of age but was reemployed in a different position.

Staff ( As of September 1, 2015 )Director-General

Tomoyuki HIGUCHI

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Satoshi ITO

Vice Director-General

Koji KANEFUJI

Department of Statistical Modeling
Director Junji NAKANO• Spatial and Time Series Modeling Group —————————————————————————————————————————————————————
Prof.

Nobuhisa KASHIWAGIProf.

Tomoyuki HIGUCHI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Genta UENO

Assist. Prof.

Shinya NAKANO

Project Researcher

Ai KAWAMORI• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.

Yoshiyasu TAMURA

Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Shinsuke KOYAMA

Assist. Prof.

Ayaka SAKATA

Visiting Prof.

Michiko WATANABE

Visiting Prof.

Kenichi MIURA

Visiting Prof.

Hiroko NAKANISHI

Visiting Assoc. Prof. Toru ONODERA• Latent Structure Modeling Group ——————————————————————————————————————————————————————————Prof.

Hiroshi MARUYAMAProf.

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Tomoko MATSUIProf.

Kazuhiro MINAMI

Project Assist. Prof. Akira TAMAMORI

Yoshinori KAWASAKI

Department of Data Science
Director Satoshi YAMASHITA• Data Design Group ———————————————————————————————————————————————————————————————————
Prof.

Takashi NAKAMURA

Prof.

Ryozo YOSHINO

Assoc. Prof.

Naomasa MARUYAMA

Assoc. Prof.

Tadahiko MAEDA

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Masayo HIROSE

Visiting Prof.

Takatoshi IMADA

25

Department of Data Science• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ikuko FUNATOGAWA

Assist. Prof.

Nobuo SHIMIZU

Assist. Prof.

Hisashi NOMA

• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.

Koji KANEFUJI

Assist. Prof.

Yoo Sung PARK

Assoc. Prof.

Jun ADACHI

Assoc. Prof.

Manabu KUROKI

Department of Mathematical Analysis and Statistical Inference
Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Kei KOBAYASHI

Assist. Prof.

Teppei OGIHARA• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Prof.

Hironori FUJISAWA

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Project Assist. Prof. Osamu KOMORI

Visiting Prof.

Ryuei NISHII

Visiting Assoc. Prof. Yoshiyuki NINOMIYA• Computational Inference Group ———————————————————————————————————————————————————————————
Prof.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Prof.

Satoshi ITO

Risk Analysis Research Center
Director Satoshi YAMASHITA
Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Shinto EGUCHIProf.

Koji KANEFUJI

Prof.

Nobuhisa KASHIWAGI

Prof.

Atsushi YOSHIMOTO

Prof.

Yoshinori KAWASAKI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Manabu KUROKI

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Shogo KATO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Hisashi NOMA

Assist. Prof.

Teppei OGIHARA

Project Assist. Prof. Tomoaki IMOTO

Project Assist. Prof. Yoshitake TAKEBAYASHI

26

Vice Director Manabu KUROKI

Project Assist. Prof. Yuta KOIKE

Visiting Prof.

Rinya TAKAHASHI

Manabu IWASAKI

Visiting Prof.

Toshiya SATO

Masaaki MATSUURA

Visiting Prof.

Satoshi TERAMUKAI

Tatsuhiko TSUNODA

Visiting Prof.

Shigeyuki MATSUIVisiting Prof.

Naoki SAKAI

Mihoko MINAMI

Visiting Prof.

Megu OTAKI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Toshihiro HORIGUCHI

Visiting Prof.

Osamu NAGAFUCHI

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Toshio HONDA

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Tadashi ONO

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Hideatsu TSUKAHARA

Visiting Prof.

Toshio ONISHI

Yo SHIINA

Visiting Prof.

Yoichi KATO

Visiting Prof.

Visiting Prof.
Visiting Prof.

Visiting Prof.
Visiting Prof.

Visiting Assoc. Prof. Hideki KATAGIRIVisiting Assoc. Prof. Koji OKUHARA

Visiting Assoc. Prof. Hisateru TACHIMORI

Visiting Assoc. Prof. Makoto TOMITA

Visiting Assoc. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Masakazu FURUKAWA

Visiting Assoc. Prof. Ryota NAKAMURA

Visiting Assoc. Prof. Toshikazu KITANO

Visiting Assoc. Prof. Hisayuki HARA

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Assoc. Prof. Yasuhiro KUBOTA

Visiting Assoc. Prof. Kenichi KAMO

Organization

Staff
Risk Analysis Research Center
Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Katsuya TANAKA

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Masakazu ANDO

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Yasutaka SHIMIZU

Visiting Assoc. Prof. Masaaki FUKASAWA

Visiting Assoc. Prof. Hitoshi MOTOYAMA

Visiting Assoc. Prof. Yoichi NISHIYAMA

Visiting Assoc. Prof. Matthew GERSTENBERGER

Project Researcher

Takao KUMAZAWA

Research and Development Center for Data Assimilation
Director Tomoyuki HIGUCHI

Vice Director Yoshiyasu TAMURA

Prof.

Tomoyuki HIGUCHIProf.

Yoshiyasu TAMURA

Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Genta UENO

Assoc. Prof.

Ryo YOSHIDA

Assist. Prof.

Shinya NAKANO

Visiting Prof.

Takashi WASHIO

Visiting Prof.

Shinichi OTANIVisiting Assoc. Prof. Kazuyuki NAKAMURA

Visiting Assoc. Prof. Hiromichi NAGAO

Visiting Assoc. Prof. Hiroshi KATO

Visiting Assoc. Prof. Osamu HIROSE

Visiting Assoc. Prof. Hiroshi YAMASHITA

Project Researcher

Yuya ARIYOSHI

Tadahiko MAEDA

Project Researcher

Kazue SUZUKI

Survey Science Center
Director Ryozo YOSHINOProf.

Ryozo YOSHINOProf.

Takashi NAKAMURA

Assoc. Prof.

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Yoo Sung PARK

Project Assist. Prof. Yusuke INAGAKIVisiting Prof.

Toru KIKKAWA

Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Masato YONEDA

Visiting Prof.

Shintaro SONO

Visiting Prof.

Kazufumi MANABE

Visiting Prof.

Fumi HAYASHI

Visiting Prof.

Masahiro MIZUTA

Visiting Assoc. Prof. Takahito ABE

Visiting Assoc. Prof. Wataru MATSUMOTO

Visiting Assoc. Prof. Koken OZAKI

Visiting Assoc. Prof. Tadayoshi FUSHIKI

Visiting Assoc. Prof. Hiroko TSUNODA

Visiting Assoc. Prof. Taisuke FUJITA

Project Researcher

Kosuke NIKAIDO

Project Researcher

Noriko MIWA

Research Center for Statistical Machine Learning
Director Kenji FUKUMIZU

Vice Director Tomoko MATSUIProf.

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yoshihiko MIYASATO

Prof.

Satoshi ITO

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shinsuke KOYAMA

Assist. Prof.

Kei KOBAYASHI

Visiting. Prof.

Takashi TSUCHIYA

Visiting. Prof.

Masataka GOTO

Project Assist. Prof. Song LIU
Visiting. Prof.

Koji TSUDA

Visiting Assoc. Prof. Arthur GRETTON
Project Researcher

Visiting Assoc. Prof. Yuji SHINANO

Visiting Assoc. Prof. Shaogao LU

Visiting Assoc. Prof. Ruriko YOSHIDA

Project Researcher

Ikumi SUZUKI

Mikio MORII

Service Science Research Center
Director Hiroshi MARUYAMA

Hiroshi MARUYAMAProf.

Tomoyuki HIGUCHIProf.

Tomoko MATSUIProf.

Junji NAKANOAssoc. Prof.

Manabu KUROKI

Assoc. Prof.

Kazuhiro MINAMI

Assist. Prof.

Nobuo SHIMIZU

Visiting Prof.

Yoichi MOTOMURA

Visiting Prof.

Shusaku TSUMOTO

Prof.

27

Service Science Research Center
Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Yoshiki YAMAGATA

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Toshihiko KAWAMURA

Visiting Assoc. Prof. Haruhisa FUKUDA

Visiting Prof.

Tadahiko SATO

Visiting Assoc. Prof. Eiji MOTOHASHI

School of Statistical Thinking
Director Yoshinori KAWASAKI

Vice Director Jun ADACHIHiroshi MARUYAMAProf.

Satoshi ITO

Prof.

Yukito IBA

Assoc. Prof.

Naomasa MARUYAMA

Assist. Prof.

Teppei OGIHARA

Assist. Prof.

Ayaka SAKATA

Assist. Prof.

Masayo HIROSE

Adjunct Professor

Yasumasa BABA

Adjunct Professor

Makio ISHIGURO

Adjunct Professor

Kunio SHIMIZU

Project Assist. Prof. Kei TAKAHASHI

Project Assist. Prof. Keiichi FUKAYA

Project Assist. Prof. Toshiya KAZAMA

Project Researcher

Naoki KAMIYA

Project Researcher

Tomoya TANJO

Prof.

Project Assist. Prof. Kaname MATSUE

Project Researchers
Project Assoc. Prof. Roberto Sebastian LEGASPIProject Assist. Prof. Kazuhiko SHIBUYA

Center for Engineering and Technical Support
Director

Yoshinori KAWASAKI

Deputy Manager

Yuriko WATANABE

Head of Computing Facilities Unit

Kazuhiro NAKAMURA

Head of Media Development Unit

Akiko NAGASHIMA

Vice Director
Senior Specialist

Jun ADACHI
Saeko TANAKA

Head of Information Resources Unit

Saeko TANAKA

Library
Head Yoshinori KAWASAKI

Administration Planning and Coordination Section
Director Tomoyuki HIGUCHIHead of Planning Unit

Satoshi ITOHead of Evaluation Unit

Yoshiyasu TAMURA

Head of Information and Public Relations Unit

Koji KANEFUJI

Head of Intellectual Property Unit

Hiroshi MARUYAMAHead of NOE Promotion Unit

Satoshi ITO• URA Station ————————————————————————————————————————————————————————————————————————
Leader

Kozo KITAMURA

SubleaderMotoi OKAMOTO

Research Administrator

Keisuke HONDA

Research Administrator

Yoko OGAWA• Office of Female Researcher Development, ISM —————————————————————————————————————————————————

Koji KANEFUJI
Kozo KITAMURA28

Yoo Sung PARK

Organization

Staff
NIPR/ISM Joint Administration OfficeDirector of NIPR/ISM Joint Administration OfficeKazuhiko HASEGAWA

Director of General Service Center

Katsunori NOZUMI• Planning Section ( ISM ) —————————————————————————————————————————————————————————————————————
Head of Planning Section Toyoharu HAYASHIDA
Deputy Head

Kazuhiko GOTO

Deputy Head

Yutaka ONO

Specialist

Fumio SUTOTeam Leader

Koji MORITATeam Leader

Hiroaki ARAI

Team Leader

Ichiro KAWAJI• Planning Section ( NIPR) ————————————————————————————————————————————————————————————————————
Head of Planning Section Michiaki NAKANO
Deputy Head

Koji SAKAMOTOTeam Leader

Kazuhisa OSHITATeam Leader

Masaki ONIZAWA• General Service Center —————————————————————————————————————————————————————————————————————
Deputy Head

Motokazu TOYODA

Deputy HeadTomohiko MIYAUCHITeam Leader

Yoshihiro YAMADATeam Leader

Yumiko OKAWA

Team Leader

Kentaro TSUJIITeam Leader

Susumu YAMAGUCHITeam Leader

Kenichi SHIOBARA

Specialist

Hitoshi HIRAYAMA

Council of The Institute of Statistical Mathematics ( As of April 1, 2015 )Yasushi AKIYAMA

Professor, Graduate School of Information Science and Engineering , Tokyo Institute of Technology

Masahiro MIZUTA

Professor, Information Initiative Center, and Graduate School of Information Science and Technology,
Hokkaido University

Shigeru OBAYASHI

Director Institute of Fluid Science Tohoku University

Nakahiro YOSHIDA

Professor, Graduate School of Mathematical Sciences, The University of Tokyo

Nobuhiko TERUIProfessor, Graduate School of Economics and Management, Tohoku University

Ryuei NISHII

Professor, Institute of Mathematics for Industry, Kyushu University

Yoshihiro YAJIMA

Professor, Graduate School of Economics, The University of Tokyo

Shoichi YOKOYAMA

Professor, Department of Linguistic Theory and Structure, National Institute for Japanese Language and Linguistics

Masato OKADA

Professor, Graduate School of Frontier Sciences, The University of Tokyo

Michiko WATANABE

Professor, The Graduate School of Health Management, Keio University

Yoshiyasu TAMURA

Professor (Vice Director-General, ISM )Satoshi ITOProfessor ( Vice Director-General, ISM )Kouji KANEFUJI

Professor ( Vice Director-General, ISM )Junji NAKANOProfessor (Director of Department of Statistical Modeling, ISM )Satoshi YAMASITA

Professor (Director of Department of Data Science, ISM )Satoshi KURIKI

Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM )Yoshinori KAWASAKI

Professor (Director of Center for Engineering and Technical Support, ISM )Nobuhisa KASHIWAGI

Professor (Department of Statistical Modeling, ISM )Hiroshi MARUYAMA

Professor (Department of Statistical Modeling, ISM )Ryozo YOSHINO

Professor (Department of Data Science, ISM )Kenji FUKUMIZU

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )29

Organization

Cooperative Research Committee ( As of April 1, 2015 )Tadahiko SATO

Professor, Faculty of Business Sciences, University of Tsukuba

Akinobu TAKEUCHI

Associate Professor, Faculty of Humanities and Social Sciences, Jissen Women’s University

Shigeyuki MATSUI

Professor, Graduate School of Medicine, Nagoya University

Hiroshi YADOHISA

Professor, Faculty of Culture and Information Science, Doshisha University

Kazue YAMAOKA

Professor, Graduate School of Public Health, Teikyo University

Junji NAKANOProfessor (Director of Department of Statistical Modeling, ISM )Satoshi YAMASHITA

Professor (Director of Department of Data Science, ISM )Satoshi ITOProfessor ( Vice Director-General, ISM )Shinto EGUCHI

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Research Ethics Review Committee ( As of April 1, 2015 )Specialist on epidemiology and social research

Kazuo SEIYAMA

Professor, School of Sociology, Kwansei Gakuin University

Specialist on epidemiology and social research

Keiko SATO

Associate professor, Kyoto University Hospital,
Institute for Advancement of Clinical and Translational Science
Department of EBM ResearchSpecialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Kindergarten Director and Elementary School Director,
KEIMEI GAKUEN

Research education staff of ISM

Takashi NAKAMURA

Professor ( Department of Data Science, ISM)

Research education staff of ISM

Koji KANEFUJI

Professor ( Vice Director-General, ISM)

Research education staff of ISM

Tadahiko MAEDA

Associate Professor ( Department of Data Science, ISM)

Research education staff of ISM

Ikuko FUNATOGAWA

Associate Professor ( Department of Data Science, ISM)

Research education staff of ISM

Shuhei MANO

Associate Professor (Department of Mathematical Analysis andStatistical Inference, ISM )Professor Emeritus ( As of April 1, 2015 )30

Kameo MATUSITA

Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social science
theories ), and the 3rd Research Dept. ( operations, research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

A new office building was constructed in Minato Ward.
The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.
The 6th Research Dept. (statistical theories of human behavior) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation of Information and Systems of the Inter-University Research Institute based on the National University Corporation Law.
The Planning Coordination Chief System was abolished and the position of Vice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Sciences were instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.
The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section (hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.

2009

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.

31

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorail—10 min walk from Takamatsu Sta.
Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/